Imagine your business is an ocean. Under the surface, a rich ecosystem of processes, people, and technology needs to work together to create a healthy environment. Above the surface, constant waves can make or break your business, either propelling you to new opportunities or sinking you under pressure.
Data fabric might seem like yet another data management technological innovation you have to learn about—but this one is different from the rest. While technology continues to evolve, the data problems that enterprises face are in many cases getting worse over time: data, data, and more data is permeating the business.
Artificial intelligence (AI) promises outsized gains in business efficiency. And since organizations live or die by their processes, integrating AI into the picture through AI process optimization can help boost ROI with end-to-end process automation and enhancements. This blog will explore five ways AI can be used to improve and optimize process efficiency. But first, let’s explain what AI process optimization means.
I’m a firm believer that banks rarely invest to drive revenue and elevate customer service. But I recently hosted a roundtable discussion in London with Appian, a process automation platform company, where leaders from mainstream and challenger banks in the UK gathered to discuss the challenges facing financial institutions when adopting new technology.
Data management in the modern enterprise requires skill and stamina. With the mountainous rise in data volume and the complexity of managing disparate data sources across a wide variety of legacy systems and hybrid cloud environments, it can seem virtually impossible to successfully climb past enterprise data obstacles to success. However, data fabric technology eliminates some of those obstacles. Is it the answer for your data strategy?
Process automation tools automate manual and repetitive tasks inside a business process. But what distinguishes process automation tools, which may also be called business process automation (BPA) tools or hyperautomation tools, from other automation solutions is that they automate an entire business process, end to end. Some process automation examples can be found in banking customer onboarding processes and automated insurance underwriting.
Automation has been a linchpin for businesses for decades. Automation tools pave the way for greater efficiency, enhanced productivity, improved customer experience, and many other benefits. Yet, in some cases, businesses have only seen small gains from their automation efforts. When this happens, it could be because organizations were focused only on automation rather than the wider concept of hyperautomation.
How can you find time to drive efficiency across your organization when there is so much digital work to be done? This is one area where hyperautomation and digital transformation initiatives intersect. Yet, just like other technology trends, our understanding of hyperautomation and its value has become convoluted. Let’s demystify some of the gray areas surrounding what hyperautomation technologies and tools can help you accomplish as you shape automation strategy and select tools.
The importance of an organization’s data strategy cannot be understated in today’s world. With the speed at which data is created across the digital landscape, organizations need to be able to harness information from a wide variety of disparate sources at a rapid pace to drive business intelligence and competitive advantage.
If you’re evaluating enterprise business process automation technologies, it’s important to understand what complete process automation technology can do compared to today’s many automation point solutions. Process automation technologies automate repetitive and manual tasks inside a business process.
